Intrinsically Motivated Learning of Hierarchical Collections of Skills Export

@inproceedings{barto_intrinsic_2004, abstract = {Humans and other animals often engage in activities for their own sakes rather than as steps toward solving practical problems. Psychologists call these intrinsically motivated behaviors. What we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated learning aimed at allowing artificial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy. At the core of the model are recent theoretical and algorithmic advances in computational reinforcement learning, specifically, new concepts related to skills and new learning algorithms for learning with skill hierarchies.}, author = {Barto, A. G. and Singh, S. and Chentanez, N.}, booktitle = {International Conference on Developmental Learning (ICDL)}, citeulike-article-id = {1693122}, citeulike-linkout-0 = {http://www-anw.cs.umass.edu/pubs/2004/barto_sc_ICDL04.pdf}, keywords = {hierarchical, intrinsic, motivational}, posted-at = {2007-09-25 15:33:51}, priority = {0}, title = {Intrinsically Motivated Learning of Hierarchical Collections of Skills}, url = {http://www-anw.cs.umass.edu/pubs/2004/barto_sc_ICDL04.pdf}, year = {2004} }

TY - CONF ID - barto_intrinsic_2004 L3 - citeulike-article-id:1693122 N2 - Humans and other animals often engage in activities for their own sakes rather than as steps toward solving practical problems. Psychologists call these intrinsically motivated behaviors. What we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated learning aimed at allowing artificial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy. At the core of the model are recent theoretical and algorithmic advances in computational reinforcement learning, specifically, new concepts related to skills and new learning algorithms for learning with skill hierarchies. TI - Intrinsically Motivated Learning of Hierarchical Collections of Skills T2 - International Conference on Developmental Learning (ICDL) KW - hierarchical KW - intrinsic KW - motivational AU - Barto, AG AU - Singh, S AU - Chentanez, N PY - 2004/// UR - http://www-anw.cs.umass.edu/pubs/2004/barto_sc_ICDL04.pdf ER -